CN103593598A - User online authentication method and system based on living body detection and face recognition - Google Patents
User online authentication method and system based on living body detection and face recognition Download PDFInfo
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- CN103593598A CN103593598A CN201310602042.8A CN201310602042A CN103593598A CN 103593598 A CN103593598 A CN 103593598A CN 201310602042 A CN201310602042 A CN 201310602042A CN 103593598 A CN103593598 A CN 103593598A
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- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
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- G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
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Abstract
The invention discloses a user online authentication method and system based on living body detection and face recognition. The method includes the user online registration step and the user online authentication step. The user online authentication step comprises the living body detection step, the image processing step, the feature value extraction step, the face comparison step and the result processing step. In the living body detection step, whether an authenticated user is a living body or not is determined and a face picture is acquired. In the image processing step, the collected face picture is processed. In the feature value extraction step, face part features of the processed face picture are extracted. In the face comparison step, extracted feature data of the collected face image are compared with corresponding face data in a user face feature value database, a threshold value is set, and when similarity exceeds the threshold value, the acquired result through matching is output. The user online authentication method and system can avoid authentication cheating through videos including faces, safety of the system is improved, recognition time can be shortened, and recognition accuracy is improved.
Description
Technical field
The invention belongs to computing machine and face recognition technology field, relate to a kind of on-line authentication recognition methods, relate in particular to a kind of user's on-line authentication method based on live body detection and recognition of face; Meanwhile, the invention still further relates to a kind of user's on-line authentication system based on live body detection and recognition of face.
Background technology
In current information society, the infotech that the cyber-net of take is representative is almost penetrated into the every aspect of life.Yet we,, when enjoying " information ", have also brought unsafe shade to people, so people, in being engaged in vairious activities, often need to carry out the authentication of personal identification, thus the security of guarantee information.Because being the naturality of people's face, stability, easy collection property, and be applied to authentication.
Existing verification process refers to Fig. 1, mainly comprises two stages: when user's online registration, need to gather user's human face photo, extract face characteristic and put it in characteristic value data storehouse; When user's on-line authentication, gather user's human face photo, then carry out the detection of people's face, image processing, face characteristic extraction, and the face characteristic extracting is compared with face characteristic corresponding in characteristic value data storehouse, then obtain a result.
Existing authentication method Shortcomings part, mainly comprises: one, in camera collection photo module, cannot screen image/video or true man; Its two, this method does not meet user's real demand at recognition speed and discrimination.
In view of this, nowadays in the urgent need to designing a kind of new on-line authentication mode, to overcome the above-mentioned defect of existing authentication method.
Summary of the invention
Technical matters to be solved by this invention is: a kind of user's on-line authentication method based on live body detection and recognition of face is provided, can avoids gaining authentication by cheating with the video that contains people's face, improve security of system; Can shorten recognition time, improve recognition accuracy simultaneously.
In addition, the present invention also provides a kind of user's on-line authentication system based on live body detection and recognition of face, can avoid gaining authentication by cheating with the video that contains people's face, improves security of system; Can shorten recognition time, improve recognition accuracy simultaneously.
For solving the problems of the technologies described above, the present invention adopts following technical scheme:
User's on-line authentication method of live body detection and recognition of face, described method comprises:
Step S10, user's online registration step: the online fill message of user, and by public security Intranet, transfer corresponding Certification of Second Generation photo according to the ID (identity number) card No. of submitting to, and extract photo face characteristic value, set up user's face characteristic Value Data storehouse;
Step S20, user's on-line steps; Comprise live body detecting step, image processing step, eigenwert extraction step, face alignment step, result treatment step; Specifically comprise:
-step S21, live body detecting step, be confirmed whether to be live body and to obtain optimum human face photo; Determining program based on head rotation direction as instruction only drives bead to reach assigned address by head rotation in setting-up time, is judged as live body, can authenticate; The photo of simultaneously choosing anglec of rotation minimum is optimum human face photo; Live body detecting step comprises that people's face detects and human face posture detects;
In people's face detecting step, determine whether it is people's face and organ location; Camera collection, to each two field picture, is carried out to greyscale transformation, filtering processing, obtain high-quality gray-scale map; Whether to gray-scale map, utilize integration to calculate fast Harr-Like wavelet character value, be applied to the AdaBoost-Cascade sorter that off-line training is good, differentiating is people's face; According to the face shape facility of people's face and AdaBoost-Cascade sorter, eyes, two eyebrow, nose, face and lower jaw location are carried out in the region of people's face window, determine human face position;
In human face posture detecting step, adopt the attitude method of estimation based on oval template and face position; First by people's face, detect the position of determining eyes, nose and face, then ellipse fitting is carried out in the people's face connected domain border detecting and obtain oval template, calculate the location parameter in template of eyes, face and nose, finally location parameter is sent into the guestimate value of the attitude parameter that three-layer artificial neural network obtains; The precision of estimating for improving attitude is sent into input picture corresponding linear dependence wave filter after certain processing according to guestimate result, obtain relatively accurate human face posture estimated result;
-step S22, image processing step; The human face photo that collects optimum attitude carries out light compensation, greyscale transformation, histogram equalization, normalization, geometry correction, filtering and sharpening, serves feature extraction;
-step S23, characteristic extraction step; To the human face photo of processing, extract face component feature, comprise naked face, eyebrow, eyes, mouth face component, utilize principal component method to extract face component feature;
-step S24, face alignment step; Characteristic and the Certification of Second Generation people face data of the facial image of the collection of extracting, by setting a threshold value, when similarity surpasses this threshold value, the result coupling being obtained output;
-step S25, result treatment step; As result coupling, prompting " authentication success ", extracts face characteristic value simultaneously and is saved in server user's face database; As result is not mated, prompting " authentication again ", restarts photo acquisition.
User's on-line authentication method of live body detection and recognition of face, described method comprises:
Step S10, user's online registration step: the online fill message of user, obtain corresponding face characteristic value, set up user's face characteristic Value Data storehouse;
Step S20, user's on-line authentication step; Comprise live body detecting step, image processing step, eigenwert extraction step, face alignment step, result treatment step; Specifically comprise:
-step S21, live body detecting step, confirm whether authenticated is live body and obtains human face photo;
-step S22, image processing step; The human face photo gathering is processed;
-step S23, characteristic extraction step; To the human face photo of processing, extract face component feature;
-step S24, face alignment step; The characteristic of the facial image of the collection of extracting and the corresponding people's face data in described user's face characteristic Value Data storehouse, by setting a threshold value, when similarity surpasses this threshold value, the result coupling being obtained output;
-step S25, result treatment step; According to face alignment result, make respective handling.
As a preferred embodiment of the present invention, in described step S21, the method that determines whether live body is: the determining program based on head rotation direction as instruction, only have by head rotation band animal body and reach assigned address, being judged as live body, to choose the photo of anglec of rotation minimum be optimum human face photo simultaneously.
As a preferred embodiment of the present invention, the live body detecting step in described step S21 comprises that people's face detects and human face posture detects;
In people's face detecting step, determine whether it is people's face and organ location; Camera collection, to each two field picture, is carried out to greyscale transformation, filtering processing, obtain high-quality gray-scale map;
Whether to gray-scale map, utilize integration to calculate fast Harr-Like wavelet character value, be applied to the AdaBoost-Cascade sorter that off-line training is good, differentiating is people's face;
According to the face shape facility of people's face and AdaBoost-Cascade sorter, eyes, two eyebrow, nose, face and lower jaw location are carried out in the region of people's face window, determine human face position;
In human face posture detecting step, adopt the attitude method of estimation based on oval template and face position;
First by people's face, detect the position of determining eyes, nose and face, then ellipse fitting is carried out in the people's face connected domain border detecting and obtain oval template, calculate the location parameter in template of eyes, face and nose, finally location parameter is sent into the guestimate value of the attitude parameter that three-layer artificial neural network obtains;
The precision of estimating for improving attitude, sends input picture into corresponding linear dependence wave filter after treatment according to guestimate result, obtains relatively accurate human face posture estimated result.
As a preferred embodiment of the present invention, in step S22, the human face photo that collects optimum attitude carries out light compensation, greyscale transformation, histogram equalization, normalization, geometry correction, filtering and sharpening, serves feature extraction;
In step S23, the face component feature of extraction comprises naked face, eyebrow, eyes, mouth face component, utilizes principal component method to extract face component feature;
In step S25, as result coupling, prompting " success ", extracts face characteristic value simultaneously and is saved in server user's face database; As result is not mated, prompting " again ", restarts photo acquisition.
User's on-line authentication system of live body detection and recognition of face, described system comprises:
User's online registration module, for the online fill message of user, and transfers corresponding Certification of Second Generation photo according to the ID (identity number) card No. of submitting to by public security Intranet, extracts photo face characteristic value, sets up user's face characteristic Value Data storehouse;
User's on-line authentication module, comprises live body detecting unit, graphics processing unit, eigenwert extraction unit, face alignment unit, result treatment unit;
Described live body detecting unit is live body and obtains optimum human face photo in order to being confirmed whether; Live body detecting unit comprises that head rotation direction is obtained subelement, position generates subelement, object of which movement driven element unit, and head rotation direction is obtained subelement in order to obtain the video of head rotation direction, and therefrom obtains the rotation direction of head; Position generates subelement in order to the position of random generation object, and the object assigned address that need to arrive; Object of which movement driven element unit, in order to drive object of which movement according to head rotation direction, only has by head rotation band animal body and reaches assigned address, is judged as live body; The photo of simultaneously choosing anglec of rotation minimum is optimum human face photo; Described live body detecting unit comprises people's face detection sub-unit and human face posture detection sub-unit;
People's face detection sub-unit is people's face and organ location in order to determine whether; Camera collection, to each two field picture, is carried out to greyscale transformation, filtering processing, obtain high-quality gray-scale map; Whether to gray-scale map, utilize integration to calculate fast Harr-Like wavelet character value, be applied to the AdaBoost-Cascade sorter that off-line training is good, differentiating is people's face; According to the face shape facility of people's face and AdaBoost-Cascade sorter, eyes, two eyebrow, nose, face and lower jaw location are carried out in the region of people's face window, determine human face position;
Human face posture detection sub-unit is in order to adopt the attitude method of estimation based on oval template and face position to carry out attitude detection; By people's face, detect the position of determining eyes, nose and face, ellipse fitting is carried out in the people's face connected domain border detecting and obtain oval template, calculate the location parameter in template of eyes, face and nose, location parameter is sent into the guestimate value of the attitude parameter that three-layer artificial neural network obtains; The precision of estimating for improving attitude, sends input picture into corresponding linear dependence wave filter after treatment according to guestimate result, obtains relatively accurate human face posture estimated result;
Described graphics processing unit carries out light compensation, greyscale transformation, histogram equalization, normalization, geometry correction, filtering and sharpening in order to collect the human face photo of optimum attitude, serves feature extraction;
Described feature extraction unit, in order to the human face photo of processing, is extracted face component feature, comprises naked face, eyebrow, eyes, mouth face component, utilizes principal component method to extract face component feature;
Described face alignment unit is in order to characteristic and the Certification of Second Generation people face data of the facial image of the collection of extraction, and by setting a threshold value, when similarity surpasses this threshold value, the result coupling being obtained is exported;
Described result treatment unit is in order to make respective handling according to the comparison result of face alignment unit; As result coupling, prompting " authentication success ", extracts face characteristic value simultaneously and is saved in server user's face database; As result is not mated, prompting " authentication again ", restarts photo acquisition.
User's on-line authentication system of live body detection and recognition of face, described system comprises:
-user online registration module, for the online fill message of user, obtains corresponding face characteristic value, and sets up user's face characteristic Value Data storehouse;
-user on-line authentication module, comprises live body detecting unit, graphics processing unit, eigenwert extraction unit, face alignment unit, result treatment unit;
Described live body detecting unit is in order to confirm whether authenticated is live body and obtains human face photo;
Described graphics processing unit is in order to process the human face photo gathering;
Described feature extraction unit, in order to the human face photo of processing, is extracted face component feature;
Described face alignment unit is in order to the characteristic of the facial image of the collection of extracting and corresponding people's face data in described user's face characteristic Value Data storehouse, and by setting a threshold value, when similarity surpasses this threshold value, the result coupling being obtained is exported;
Described result treatment unit is in order to make respective handling according to face alignment result.
As a preferred embodiment of the present invention, described user's online registration module is transferred corresponding Certification of Second Generation photo according to the ID (identity number) card No. of submitting to by public security Intranet, extracts photo face characteristic value, sets up user's face characteristic Value Data storehouse.
As a preferred embodiment of the present invention, described live body detecting unit is live body and obtains optimum human face photo in order to being confirmed whether; Live body detecting unit comprises that head rotation direction is obtained subelement, position generates subelement, object of which movement driven element unit, and head rotation direction is obtained subelement in order to obtain the video of head rotation direction, and therefrom obtains the rotation direction of head; Position generates subelement in order to the position of random generation object, and the object assigned address that need to arrive; Object of which movement driven element unit, in order to drive object of which movement according to head rotation direction, only has by head rotation band animal body and reaches assigned address, is judged as live body; The photo of simultaneously choosing anglec of rotation minimum is optimum human face photo; Described live body detecting unit comprises people's face detection sub-unit and human face posture detection sub-unit;
People's face detection sub-unit is people's face and organ location in order to determine whether; Camera collection, to each two field picture, is carried out to greyscale transformation, filtering processing, obtain high-quality gray-scale map; Whether to gray-scale map, utilize integration to calculate fast Harr-Like wavelet character value, be applied to the AdaBoost-Cascade sorter that off-line training is good, differentiating is people's face; According to the face shape facility of people's face and AdaBoost-Cascade sorter, eyes, two eyebrow, nose, face and lower jaw location are carried out in the region of people's face window, determine human face position;
Human face posture detection sub-unit is in order to adopt the attitude method of estimation based on oval template and face position to carry out attitude detection; By people's face, detect the position of determining eyes, nose and face, ellipse fitting is carried out in the people's face connected domain border detecting and obtain oval template, calculate the location parameter in template of eyes, face and nose, location parameter is sent into the guestimate value of the attitude parameter that three-layer artificial neural network obtains; The precision of estimating for improving attitude, sends input picture into corresponding linear dependence wave filter after treatment according to guestimate result, obtains relatively accurate human face posture estimated result.
As a preferred embodiment of the present invention, described graphics processing unit carries out light compensation, greyscale transformation, histogram equalization, normalization, geometry correction, filtering and sharpening in order to collect the human face photo of optimum attitude, serves feature extraction;
Described feature extraction unit, in order to the human face photo of processing, is extracted face component feature, comprises naked face, eyebrow, eyes, mouth face component, utilizes principal component method to extract face component feature;
Described face alignment unit is in order to characteristic and the Certification of Second Generation people face data of the facial image of the collection of extraction, and by setting a threshold value, when similarity surpasses this threshold value, the result coupling being obtained is exported;
Described result treatment unit is in order to make respective handling according to the comparison result of face alignment unit; As result coupling, prompting " authentication success ", extracts face characteristic value simultaneously and is saved in server user's face database; As result is not mated, prompting " authentication again ", restarts photo acquisition.
Beneficial effect of the present invention is: the user's on-line authentication method and system based on live body detection and recognition of face that the present invention proposes, can avoid gaining authentication by cheating with the video that contains people's face, and improve security of system.The present invention, by the command operating of game type, guarantees that the photo collecting is user's photo; Meanwhile, the eigenwert of the human face photo of success identity is saved in user's face database, and the comparison of one-to-many is greatly shortened recognition time and improved recognition accuracy like this.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of existing on-line authentication method.
Fig. 2 is the process flow diagram of on-line authentication method of the present invention.
Fig. 3 is the composition schematic diagram of on-line authentication system of the present invention.
Embodiment
Below in conjunction with accompanying drawing, describe the preferred embodiments of the present invention in detail.
Embodiment mono-
Refer to Fig. 1, the present invention has disclosed a kind of user's on-line authentication method based on live body detection and recognition of face, and described method comprises:
[step S10] user online registration step: the online fill message of user, and by public security Intranet, transfer corresponding Certification of Second Generation photo according to the ID (identity number) card No. of submitting to, and extract photo face characteristic value, set up user's face characteristic Value Data storehouse.
[step S20] user on-line authentication step (as, can be login authentication); Comprise live body detecting step, image processing step, eigenwert extraction step, face alignment step, result treatment step.Specifically comprise:
-step S21, live body detecting step, be confirmed whether to be live body and to obtain optimum human face photo; First on setting screen, generate the video of bead motion, and record the move track of each time point of bead; If it is identical with the combination of bead direction of motion to collect continuous human face posture combination, is judged as live body, otherwise is photo or video; The photo of simultaneously choosing anglec of rotation minimum is optimum human face photo.Another kind of live body detection mode is: the determining program based on head rotation direction as instruction, and only in setting-up time, by head rotation, drive bead to reach assigned address, be judged as live body, can authenticate.
Live body detecting step comprises that people's face detects and human face posture detects;
In people's face detecting step, determine whether it is people's face and organ location; Camera collection, to each two field picture, is carried out to greyscale transformation, filtering processing, obtain high-quality gray-scale map; Whether to gray-scale map, utilize integration to calculate fast Harr-Like wavelet character value, be applied to the AdaBoost-Cascade sorter that off-line training is good, differentiating is people's face; According to the face shape facility of people's face and AdaBoost-Cascade sorter, eyes, two eyebrow, nose, face and lower jaw location are carried out in the region of people's face window, determine human face position;
In human face posture detecting step, adopt the attitude method of estimation based on oval template and face position; First by people's face, detect the position of determining eyes, nose and face, then ellipse fitting is carried out in the people's face connected domain border detecting and obtain oval template, calculate the location parameter in template of eyes, face and nose, finally location parameter is sent into the guestimate value of the attitude parameter that three-layer artificial neural network obtains; The precision of estimating for improving attitude is sent into input picture corresponding linear dependence wave filter after certain processing according to guestimate result, obtain relatively accurate human face posture estimated result;
-step S22, image processing step; The human face photo that collects optimum attitude carries out light compensation, greyscale transformation, histogram equalization, normalization, geometry correction, filtering and sharpening, serves feature extraction;
-step S23, characteristic extraction step; To the human face photo of processing, extract face component feature, comprise naked face, eyebrow, eyes, mouth face component, utilize principal component method to extract face component feature;
-step S24, face alignment step; Characteristic and the Certification of Second Generation people face data of the facial image of the collection of extracting, by setting a threshold value, when similarity surpasses this threshold value, the result coupling being obtained output;
-step S25, result treatment step; As result coupling, prompting " authentication success ", extracts face characteristic value simultaneously and is saved in server user's face database; As result is not mated, prompting " authentication again ", restarts photo acquisition.
More than introduced the flow process of the user's on-line authentication method that the present invention is based on live body detection and recognition of face, the present invention, when disclosing said method, also discloses a kind of user's on-line authentication system based on live body detection and recognition of face; Described system comprises: user's online registration module, user's on-line authentication module.
User's online registration module is for the online fill message of user, and by public security Intranet, transfer corresponding Certification of Second Generation photo (can certainly be other human face photos) according to the ID (identity number) card No. of submitting to, extract photo face characteristic value, set up user's face characteristic Value Data storehouse.
User's on-line authentication module comprises live body detecting unit, graphics processing unit, eigenwert extraction unit, face alignment unit, result treatment unit.
Described live body detecting unit is live body and obtains optimum human face photo in order to being confirmed whether; Live body detecting unit comprises that object of which movement presents subelement, in order to generate the video of bead motion on setting screen, live body detecting unit records the move track of each time point of bead, and changes according to human face posture whether the human face photo that judgement gathers is live body photo; If it is identical with the combination of bead direction of motion to collect continuous human face posture combination, is judged as live body, otherwise is photo or video; The photo of simultaneously choosing anglec of rotation minimum is optimum human face photo.In addition, can also detect by other means live body, as comprising head rotation direction, live body detecting unit obtains subelement, position generation subelement, object of which movement driven element unit, head rotation direction is obtained subelement in order to obtain the video of head rotation direction, and therefrom obtains the rotation direction of head; Position generates subelement in order to the position of random generation object, and the object assigned address that need to arrive; Object of which movement driven element unit, in order to drive object of which movement according to head rotation direction, only has by head rotation band animal body and reaches assigned address, is judged as live body.
Described live body detecting unit comprises people's face detection sub-unit and human face posture detection sub-unit.
People's face detection sub-unit is people's face and organ location in order to determine whether; Camera collection, to each two field picture, is carried out to greyscale transformation, filtering processing, obtain high-quality gray-scale map; Whether to gray-scale map, utilize integration to calculate fast Harr-Like wavelet character value, be applied to the AdaBoost-Cascade sorter that off-line training is good, differentiating is people's face; According to the face shape facility of people's face and AdaBoost-Cascade sorter, eyes, two eyebrow, nose, face and lower jaw location are carried out in the region of people's face window, determine human face position.
Human face posture detection sub-unit is in order to adopt the attitude method of estimation based on oval template and face position to carry out attitude detection; By people's face, detect the position of determining eyes, nose and face, ellipse fitting is carried out in the people's face connected domain border detecting and obtain oval template, calculate the location parameter in template of eyes, face and nose, location parameter is sent into the guestimate value of the attitude parameter that three-layer artificial neural network obtains; The precision of estimating for improving attitude, sends input picture into corresponding linear dependence wave filter after treatment according to guestimate result, obtains relatively accurate human face posture estimated result.
Described graphics processing unit carries out light compensation, greyscale transformation, histogram equalization, normalization, geometry correction, filtering and sharpening in order to collect the human face photo of optimum attitude, serves feature extraction.
Described feature extraction unit, in order to the human face photo of processing, is extracted face component feature, comprises naked face, eyebrow, eyes, mouth face component, utilizes principal component method to extract face component feature.
Described face alignment unit is in order to characteristic and the Certification of Second Generation people face data of the facial image of the collection of extraction, and by setting a threshold value, when similarity surpasses this threshold value, the result coupling being obtained is exported.
Described result treatment unit is in order to make respective handling according to the comparison result of face alignment unit; As result coupling, prompting " authentication success ", extracts face characteristic value simultaneously and is saved in server user's face database; As result is not mated, prompting " authentication again ", restarts photo acquisition.
In sum, the user's on-line authentication method and system based on live body detection and recognition of face that the present invention proposes, can avoid gaining authentication by cheating with the video that contains people's face, improve security of system.The present invention, by the command operating of game type, guarantees that the photo collecting is user's photo; Meanwhile, the eigenwert of the human face photo of success identity is saved in user's face database, and the comparison of one-to-many is greatly shortened recognition time and improved recognition accuracy like this.
Here description of the invention and application is illustrative, not wants by scope restriction of the present invention in the above-described embodiments.Here the distortion of disclosed embodiment and change is possible, and for those those of ordinary skill in the art, the various parts of the replacement of embodiment and equivalence are known.Those skilled in the art are noted that in the situation that not departing from spirit of the present invention or essential characteristic, and the present invention can be with other form, structure, layout, ratio, and realizes with other assembly, material and parts.In the situation that not departing from the scope of the invention and spirit, can carry out other distortion and change to disclosed embodiment here.
Claims (10)
1. the user's on-line authentication method based on live body detection and recognition of face, is characterized in that, described method comprises:
Step S10, user's online registration step: the online fill message of user, and by public security Intranet, transfer corresponding Certification of Second Generation photo according to the ID (identity number) card No. of submitting to, and extract photo face characteristic value, set up user's face characteristic Value Data storehouse;
Step S20, user's on-line authentication step; Comprise live body detecting step, image processing step, eigenwert extraction step, face alignment step, result treatment step; Specifically comprise:
-step S21, live body detecting step, be confirmed whether to be live body and to obtain optimum human face photo; Determining program based on head rotation direction as instruction only drives bead to reach assigned address by head rotation in setting-up time, is judged as live body, can authenticate; Choose the optimum human face photo of attitude simultaneously; Live body detecting step comprises that people's face detects and human face posture detects;
In people's face detecting step, determine whether it is people's face and organ location; Camera collection, to each two field picture, is carried out to greyscale transformation, filtering processing, obtain high-quality gray-scale map; Whether to gray-scale map, utilize integration to calculate fast Harr-Like wavelet character value, be applied to the AdaBoost-Cascade sorter that off-line training is good, differentiating is people's face; According to the face shape facility of people's face and AdaBoost-Cascade sorter, eyes, two eyebrow, nose, face and lower jaw location are carried out in the region of people's face window, determine human face position;
In human face posture detecting step, adopt the attitude method of estimation based on oval template and face position; First by people's face, detect the position of determining eyes, nose and face, then ellipse fitting is carried out in the people's face connected domain border detecting and obtain oval template, calculate the location parameter in template of eyes, face and nose, finally location parameter is sent into the guestimate value of the attitude parameter that three-layer artificial neural network obtains; The precision of estimating for improving attitude, sends input picture into neighborhood weighting filter after treatment according to guestimate result, obtains relatively accurate human face posture estimated result;
-step S22, image processing step; The human face photo that collects optimum attitude carries out light compensation, greyscale transformation, histogram equalization, normalization, geometry correction, filtering and sharpening, serves feature extraction;
-step S23, characteristic extraction step; To the human face photo of processing, extract face component feature, comprise naked face, eyebrow, eyes, mouth face component, utilize principal component method to extract face component feature;
-step S24, face alignment step; Characteristic and the Certification of Second Generation people face data of the facial image of the collection of extracting, by setting a threshold value, when similarity surpasses this threshold value, the result coupling being obtained output;
-step S25, result treatment step; As result coupling, prompting " authentication success ", extracts face characteristic value simultaneously and is saved in server user's face database; As result is not mated, prompting " authentication again ", restarts photo acquisition.
2. the user's on-line authentication method based on live body detection and recognition of face, is characterized in that, described method comprises:
Step S10, user's online registration step: the online fill message of user, obtain corresponding face characteristic value, set up user's face characteristic Value Data storehouse;
Step S20, user's on-line authentication step; Comprise live body detecting step, image processing step, eigenwert extraction step, face alignment step, result treatment step; Specifically comprise:
-step S21, live body detecting step, confirm whether authenticated is live body and obtains human face photo;
-step S22, image processing step; The human face photo gathering is processed;
-step S23, characteristic extraction step; To the human face photo of processing, extract face component feature;
-step S24, face alignment step; The characteristic of the facial image of the collection of extracting and the corresponding people's face data in described user's face characteristic Value Data storehouse, by setting a threshold value, when similarity surpasses this threshold value, the result coupling being obtained output;
-step S25, result treatment step; According to face alignment result, make respective handling.
3. according to claim 2ly based on live body, detect and user's on-line authentication method of recognition of face, it is characterized in that:
In described step S21, determine whether that the method for live body is: the determining program based on head rotation direction as instruction, only have by head rotation band animal body and reach assigned address, be judged as live body; The photo of simultaneously choosing anglec of rotation minimum is optimum human face photo.
4. according to claim 3ly based on live body, detect and user's on-line authentication method of recognition of face, it is characterized in that:
Live body detecting step in described step S21 comprises that people's face detects and human face posture detects;
In people's face detecting step, determine whether it is people's face and organ location; Camera collection, to each two field picture, is carried out to greyscale transformation, filtering processing, obtain high-quality gray-scale map;
Whether to gray-scale map, utilize integration to calculate fast Harr-Like wavelet character value, be applied to the AdaBoost-Cascade sorter that off-line training is good, differentiating is people's face;
According to the face shape facility of people's face and AdaBoost-Cascade sorter, eyes, two eyebrow, nose, face and lower jaw location are carried out in the region of people's face window, determine human face position;
In human face posture detecting step, adopt the attitude method of estimation based on oval template and face position;
First by people's face, detect the position of determining eyes, nose and face, then ellipse fitting is carried out in the people's face connected domain border detecting and obtain oval template, calculate the location parameter in template of eyes, face and nose, finally location parameter is sent into the guestimate value of the attitude parameter that three-layer artificial neural network obtains;
The precision of estimating for improving attitude, sends input picture into corresponding linear dependence wave filter after treatment according to guestimate result, obtains relatively accurate human face posture estimated result.
5. according to claim 2ly based on live body, detect and user's on-line authentication method of recognition of face, it is characterized in that:
In step S22, the human face photo that collects optimum attitude carries out light compensation, greyscale transformation, histogram equalization, normalization, geometry correction, filtering and sharpening, serves feature extraction;
In step S23, the face component feature of extraction comprises naked face, eyebrow, eyes, mouth face component, utilizes principal component method to extract face component feature;
In step S25, as result coupling, prompting " authentication success ", extracts face characteristic value simultaneously and is saved in server user's face database; As result is not mated, prompting " authentication again ", restarts photo acquisition.
6. the user's on-line authentication system based on live body detection and recognition of face, is characterized in that, described system comprises:
User's online registration module, for the online fill message of user, and transfers corresponding Certification of Second Generation photo according to the ID (identity number) card No. of submitting to by public security Intranet, extracts photo face characteristic value, sets up user's face characteristic Value Data storehouse;
User's on-line authentication module, comprises live body detecting unit, graphics processing unit, eigenwert extraction unit, face alignment unit, result treatment unit;
Described live body detecting unit is live body and obtains optimum human face photo in order to being confirmed whether; Live body detecting unit comprises that head rotation direction is obtained subelement, position generates subelement, object of which movement driven element unit, and head rotation direction is obtained subelement in order to obtain the video of head rotation direction, and therefrom obtains the rotation direction of head; Position generates subelement in order to the position of random generation object, and the object assigned address that need to arrive; Object of which movement driven element unit, in order to drive object of which movement according to head rotation direction, only has by head rotation band animal body and reaches assigned address, is judged as live body; The photo of simultaneously choosing anglec of rotation minimum is optimum human face photo; Described live body detecting unit comprises people's face detection sub-unit and human face posture detection sub-unit;
People's face detection sub-unit is people's face and organ location in order to determine whether; Camera collection, to each two field picture, is carried out to greyscale transformation, filtering processing, obtain high-quality gray-scale map; Whether to gray-scale map, utilize integration to calculate fast Harr-Like wavelet character value, be applied to the AdaBoost-Cascade sorter that off-line training is good, differentiating is people's face; According to the face shape facility of people's face and AdaBoost-Cascade sorter, eyes, two eyebrow, nose, face and lower jaw location are carried out in the region of people's face window, determine human face position;
Human face posture detection sub-unit is in order to adopt the attitude method of estimation based on oval template and face position to carry out attitude detection; By people's face, detect the position of determining eyes, nose and face, ellipse fitting is carried out in the people's face connected domain border detecting and obtain oval template, calculate the location parameter in template of eyes, face and nose, location parameter is sent into the guestimate value of the attitude parameter that three-layer artificial neural network obtains; The precision of estimating for improving attitude, sends input picture into corresponding linear dependence wave filter after treatment according to guestimate result, obtains relatively accurate human face posture estimated result;
Described graphics processing unit carries out light compensation, greyscale transformation, histogram equalization, normalization, geometry correction, filtering and sharpening in order to collect the human face photo of optimum attitude, serves feature extraction;
Described feature extraction unit, in order to the human face photo of processing, is extracted face component feature, comprises naked face, eyebrow, eyes, mouth face component, utilizes principal component method to extract face component feature;
Described face alignment unit is in order to characteristic and the Certification of Second Generation people face data of the facial image of the collection of extraction, and by setting a threshold value, when similarity surpasses this threshold value, the result coupling being obtained is exported;
Described result treatment unit is in order to make respective handling according to the comparison result of face alignment unit; As result coupling, prompting " authentication success ", extracts face characteristic value simultaneously and is saved in server user's face database; As result is not mated, prompting " authentication again ", restarts photo acquisition.
7. the user's on-line authentication system based on live body detection and recognition of face, is characterized in that, described system comprises:
-user online registration module, for the online fill message of user, obtains corresponding face characteristic value, and sets up user's face characteristic Value Data storehouse;
-user on-line authentication module, comprises live body detecting unit, graphics processing unit, eigenwert extraction unit, face alignment unit, result treatment unit;
Described live body detecting unit is in order to confirm whether authenticated is live body and obtains human face photo;
Described graphics processing unit is in order to process the human face photo gathering;
Described feature extraction unit, in order to the human face photo of processing, is extracted face component feature;
Described face alignment unit is in order to the characteristic of the facial image of the collection of extracting and corresponding people's face data in described user's face characteristic Value Data storehouse, and by setting a threshold value, when similarity surpasses this threshold value, the result coupling being obtained is exported;
Described result treatment unit is in order to make respective handling according to face alignment result.
8. according to claim 7ly based on live body, detect and user's on-line authentication system of recognition of face, it is characterized in that:
Described user's online registration module is transferred corresponding Certification of Second Generation photo according to the ID (identity number) card No. of submitting to by public security Intranet, extracts photo face characteristic value, sets up user's face characteristic Value Data storehouse.
9. according to claim 7ly based on live body, detect and user's on-line authentication system of recognition of face, it is characterized in that:
Described live body detecting unit is live body and obtains optimum human face photo in order to being confirmed whether; Live body detecting unit comprises that head rotation direction is obtained subelement, position generates subelement, object of which movement driven element unit, and head rotation direction is obtained subelement in order to obtain the video of head rotation direction, and therefrom obtains the rotation direction of head; Position generates subelement in order to the position of random generation object, and the object assigned address that need to arrive; Object of which movement driven element unit, in order to drive object of which movement according to head rotation direction, only has by head rotation band animal body and reaches assigned address, is judged as live body; The photo of simultaneously choosing anglec of rotation minimum is optimum human face photo; Described live body detecting unit comprises people's face detection sub-unit and human face posture detection sub-unit;
People's face detection sub-unit is people's face and organ location in order to determine whether; Camera collection, to each two field picture, is carried out to greyscale transformation, filtering processing, obtain high-quality gray-scale map; Whether to gray-scale map, utilize integration to calculate fast Harr-Like wavelet character value, be applied to the AdaBoost-Cascade sorter that off-line training is good, differentiating is people's face; According to the face shape facility of people's face and AdaBoost-Cascade sorter, eyes, two eyebrow, nose, face and lower jaw location are carried out in the region of people's face window, determine human face position;
Human face posture detection sub-unit is in order to adopt the attitude method of estimation based on oval template and face position to carry out attitude detection; By people's face, detect the position of determining eyes, nose and face, ellipse fitting is carried out in the people's face connected domain border detecting and obtain oval template, calculate the location parameter in template of eyes, face and nose, location parameter is sent into the guestimate value of the attitude parameter that three-layer artificial neural network obtains; The precision of estimating for improving attitude, sends input picture into corresponding linear dependence wave filter after treatment according to guestimate result, obtains relatively accurate human face posture estimated result.
10. according to claim 7ly based on live body, detect and user's on-line authentication system of recognition of face, it is characterized in that:
Described graphics processing unit carries out light compensation, greyscale transformation, histogram equalization, normalization, geometry correction, filtering and sharpening in order to collect the human face photo of optimum attitude, serves feature extraction;
Described feature extraction unit, in order to the human face photo of processing, is extracted face component feature, comprises naked face, eyebrow, eyes, mouth face component, utilizes principal component method to extract face component feature;
Described face alignment unit is in order to the characteristic of the facial image of the collection of extracting and corresponding people's face data in described user's face characteristic Value Data storehouse, and by setting a threshold value, when similarity surpasses this threshold value, the result coupling being obtained is exported;
Described result treatment unit is in order to make respective handling according to the comparison result of face alignment unit; As result coupling, prompting " authentication success ", extracts face characteristic value simultaneously and is saved in server user's face database; As result is not mated, prompting " authentication again ", restarts photo acquisition.
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| CN113536869A (en) * | 2020-04-17 | 2021-10-22 | 技嘉科技股份有限公司 | Face recognition device and face recognition method |
| CN113569622A (en) * | 2021-06-09 | 2021-10-29 | 北京旷视科技有限公司 | Living body detection method, device and system based on webpage and electronic equipment |
| CN114241588A (en) * | 2022-02-24 | 2022-03-25 | 北京锐融天下科技股份有限公司 | Self-adaptive face comparison method and system |
| CN114731280A (en) * | 2022-02-25 | 2022-07-08 | 百果园技术(新加坡)有限公司 | Identity authentication method, device, terminal, storage medium and program product |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030198368A1 (en) * | 2002-04-23 | 2003-10-23 | Samsung Electronics Co., Ltd. | Method for verifying users and updating database, and face verification system using the same |
| JP3841482B2 (en) * | 1996-06-18 | 2006-11-01 | 松下電器産業株式会社 | Face image recognition device |
| CN101159016A (en) * | 2007-11-26 | 2008-04-09 | 清华大学 | Living body detecting method and system based on human face physiologic moving |
| CN102789572A (en) * | 2012-06-26 | 2012-11-21 | 五邑大学 | Living body face safety certification device and living body face safety certification method |
| CN103116763A (en) * | 2013-01-30 | 2013-05-22 | 宁波大学 | Vivo-face detection method based on HSV (hue, saturation, value) color space statistical characteristics |
-
2013
- 2013-11-25 CN CN201310602042.8A patent/CN103593598B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3841482B2 (en) * | 1996-06-18 | 2006-11-01 | 松下電器産業株式会社 | Face image recognition device |
| US20030198368A1 (en) * | 2002-04-23 | 2003-10-23 | Samsung Electronics Co., Ltd. | Method for verifying users and updating database, and face verification system using the same |
| CN101159016A (en) * | 2007-11-26 | 2008-04-09 | 清华大学 | Living body detecting method and system based on human face physiologic moving |
| CN102789572A (en) * | 2012-06-26 | 2012-11-21 | 五邑大学 | Living body face safety certification device and living body face safety certification method |
| CN103116763A (en) * | 2013-01-30 | 2013-05-22 | 宁波大学 | Vivo-face detection method based on HSV (hue, saturation, value) color space statistical characteristics |
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| CN107995979A (en) * | 2015-04-16 | 2018-05-04 | 托比股份公司 | Use the user's identification and/or certification for staring information |
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| WO2017000217A1 (en) * | 2015-06-30 | 2017-01-05 | 北京旷视科技有限公司 | Living-body detection method and device and computer program product |
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| CN105243378A (en) * | 2015-11-13 | 2016-01-13 | 清华大学 | Method and device of living body face detection on the basis of eyes information |
| CN106778454A (en) * | 2015-11-25 | 2017-05-31 | 腾讯科技（深圳）有限公司 | The method and apparatus of recognition of face |
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